President Trump wants to overhaul the U.S. immigration system so that it stops favoring visa applicants with U.S. family ties and instead gives priority to highly skilled applicants and those with job offers.94 His proposal is based on the assumption that immigrants’ educational credentials—what the administration calls “merit”—will lead to increased U.S. wages and immigrants who better integrate into U.S. culture.

Immediately after it was announced, the proposal drew criticism from all sides. Many Republicans don’t think it goes far enough to combat illegal immigration, while others on the right want total immigrant admissions to be cut. Many Democrats, meanwhile, don’t want to roll back the system’s humanitarian, family‐​oriented components and also want to resolve the status of America’s 11 million undocumented immigrants.

The whole idea of “merit” is a lightning rod in a highly charged issue. But there’s a smarter way for both sides to think about whom we let in, and why. The United States needs a new way to evaluate immigrants that predicts their future success as Americans. Indeed, the consideration of future immigrants’ contributions is now commonplace in immigration policies around the world. But the Trump administration’s idea of how to do this is too basic. Many Democrats, for their part, are too quick to dismiss any kind of evaluation as antihumanitarian. But done right, merit‐​based admissions would consider family and other humanitarian factors and likely set up new arrivals for greater success in their new country.

Call it “Immigration Moneyball”—it has two goals: to discover what factors matter to immigrant success by analyzing data and to select immigrants accordingly.

The Case for Immigration Moneyball

Just as a multidimensional data‐​driven system for evaluating and selecting players revolutionized baseball, the United States could be analyzing far more information than it currently does to decide which immigrants would best thrive in American society and contribute to the economy.

An Immigration Moneyball system would consider immigrants more fully as individuals, rather than simply as skilled workers, unskilled workers, or family members, as our current framework does. It might, for example, find merit in whether applicants made previous visits to the United States as students, tourists, or temporary workers. Imagine a system that also tracked people’s exits from our airports, harbors, and train stations and then assigned value to immigrants who left the United States when the terms of their previous visas ran out. The system might find “merit” in youth, in fluent multilingualism, in training or work experience in trades that are in special demand, or in advanced degrees from American universities. It could prioritize immigrants who pledge to settle in rapidly depopulating regions for their first 10 years after arrival, matching them to locales where they have a better chance of integrating and becoming employed.

Imagine if such factors were considered alongside whether immigrants have family in the United States to receive them, help them adjust, and help them find work. That, too, is a predictor of likely success as the wages for immigrants with close existing family ties eventually converge with immigrants admitted on labor visas that screen for credentials and contracts.95 In this light, family also represents a powerful form of merit. And because welcoming people in need is a core tenet of American culture, the criteria might include whether admission would rescue them from countries subject to severe poverty, violence, or natural disasters.

We now have the statistical tools to discover what qualities and factors make immigrants most likely to succeed in the United States and then to assess would‐​be immigrants based on those criteria. To call the approach “Moneyball” is oversimplifying, of course; immigration, unlike baseball, doesn’t deliver easily countable runs and wins. And an immigrant’s success can and should be defined in many ways. It could mean employment, economic mobility, business ownership, patents filed, sense of belonging, no criminal activity, or political participation.

What qualities matter most would largely be up to the government in power to decide. While we can debate what constitutes successful integration, it would be better if we actually collected and consolidated information about the extent to which admitted immigrants are making progress in these different ways. If we did, we could see which attributes known at admission best predict these different forms of success and adjust our criteria accordingly. Right now, we don’t know.

Immigration Admissions As Baseball Scouting

For decades, baseball was managed according to hunches and instinct. For a sport that collects more statistics than any other, much of its recruiting and game‐​day decisionmaking was highly subjective.

This all ended in 2002 when the Oakland Athletics began incorporating evidence‐​based, analytical reasoning into decisionmaking, a process now adopted to some extent by every Major League Baseball team.96 Early adopters enjoyed a big advantage before other teams caught up.

Today, U.S. immigration policy looks a lot like baseball once did. Most people form their policy preferences around their gut feelings about immigrants. Many on the left view immigrants as either hard workers who will reinforce shrinking populations or vulnerable people who must be welcomed in the spirit of humanitarianism. On the nationalist right, many view immigrants as opportunists or even criminals who come to exploit the resources of rich coun‐ tries and whose presence threatens the national culture.

Immigrants on average are powerful generators of economic growth, disproportionately employed, innovative, entrepreneurial, and law‐​abiding. Generally, they quickly integrate and do not compete with American‐​born workers for jobs, except at the lowest wages.97 Immigration advocates have repeated these findings more than a pitcher rehearses his windup.

But the current system is also operating with a clunky, outdated selection strategy. This is where critics have an important point: the system we have really is relatively indiscriminate and unconcerned with predicting good outcomes. Like baseball teams, governments and researchers already collect extensive data about admitted and prospective immigrants in every dimension of public debate: employ ment, welfare consumption, criminality, civic engagement, language attainment, educational achievement, and much more.

Officials in the U.S. Department of Homeland Security or other agencies could crunch the numbers to determine the full range of qualities and factors that help an immigrant succeed and contribute. This includes, for example, how much English‐​speaking skills upon entry matter for longer‐​term workforce participation and whether younger skilled immigrants contribute more tax dollars before retire ment than older skilled immigrants with more estab lished expertise. Officials could then evaluate the entirety of their qualifications rather than focusing on a few characteristics, such as family ties or education. The current focus reduces each immigrant—a person with unique potential and confluence of skills, attributes, and needs—to a single, artificial classification and then files him into a column on a spreadsheet. While particular H-1B visas, for example, might prioritize immigrants who have specific skills and jobs, they take no account of humanitarian concerns or whether immigrants have family in the United States.

Would you rather grant admission to an engineer based on no other information or to an engineer who speaks fluent English, has a sister in Detroit, and was once a high school exchange student in Omaha? The answer seems obvious. Perhaps less obvious, would you rather grant admission to a qualified engineer without family ties or demonstrated familiarity with the United States or to an agricultural worker who speaks proficient English, has a sister in Detroit, and was once a high school exchange student in Omaha? More difficult still, would you rather admit that agricultural worker with family ties and English skills or one with a contract offer who has agreed to settle in a rapidly depopulating region but who doesn’t have family in the United States?

An Immigration Moneyball system informed by statistical reasoning and criteria adjustable to current needs would answer these questions better than the current system. It would select optimal applicants for temporary or permanent visas based on reliable predictions about the applicants’ productivity and social contributions, as well as the state of the U.S. economy and labor market. Ambiguities will always exist, and some cases will be impossible to answer, even with the best data and statistical methods. But Immigration Moneyball gets the United States closer to a true merit‐​based system. Backed by such reasoning, the engineer or agricultural worker selected under such a system doesn’t just “look good”; we will have evidence that she is likely to be good.

How An Immigration Moneyball System Would Function

Implementing an Immigration Moneyball approach would require an overhaul of the existing admissions system to replace the way we currently admit people on labor visas and admit family members. In addition to verifying the applicant data we already collect, the government would need to collect more and then build the capacity to quickly process admissions decisions. It would also be useful to have a system that processes exits, as well as entries, and studies immigrants’ progress once they are admitted.

Once built, the system could be adjusted to fill labor gaps, respond to new research findings, or accommodate government orders with agility. Imagine if there were a shortage of nurses or programmers, if fertility rates dropped and Social Security neared insolvency, or if there were a pool of immigrants already with American university credentials who were qualified for open jobs. All of these are knowable, and a more advanced system would enable quicker adjustments.

An Immigration Moneyball system could be mostly easily implemented by creating an independent immigration admissions council to govern it. The authorizing legislation might allow both major parties to appoint three individuals for staggered five‐​year terms, as with the Federal Reserve Board of Governors. Like the Fed, the council would hold the power to adjust the distribution of point values on a quarterly basis to accommodate national interests by adjusting an immigration selection algorithm. Congress would also be involved by assigning weights to immediate family members and the total number of immigrants admitted. The council could both consider advocacy and evidence presented not only by large employers and industrial associations but also by unions, governments, and universities monitoring trends, all of which might inform the council’s decisions.

Such a system could also help the country incorporate more temporary guest worker visas that permit immigrants to regularly or seasonally enter the United States for specific work purposes and then return to their countries of origin. If government agencies ever synchronize their data, renewing a visa for these and other temporary immigrants could be more like renewing a driver’s license, subject to a variety of quick checks. This would reduce the incentive to cross the border illegally or overstay a visa. Today, employers with low‐​skilled or seasonal labor needs often rely on illegal immigrant workers, who cannot return to the United States if they return to their country of origin and are barred from returning for 10 years thanks to “unlawful presence” rules.98

The idea that what we know about immigrants when they apply for admission predicts their ultimate social and economic contributions is one that has informed immigrant admissions in places such as Australia and Canada, whose “point‐​based” systems evaluate applicants based on similar skill‐​oriented criteria. A more advanced alternative could solicit residency pledges for special applicants such as physicians and small business entrepreneurs. This could even include low‐​skilled workers with family in regions with relevant labor shortages in agriculture or construction, based on the determinations of the council.

The council could also recognize more extended members of American families. Additionally, the council might adjust visa qualifications to hit annual flow targets from different parts of the world. Properly designed, an Immigration Moneyball system should appeal to many on both sides. It would afford a greater sense of control over admissions to those on the political right and justify maintaining a steady flow of newcomers as the political left wants. As in baseball, there would be plenty of exceptions and failures. Few immigrants will create as many jobs as Elon Musk; some will commit crimes (likely at lower rates than American citizens, according to U.S. government data).99

The current immigration debate is dominated by border walls and tactics as barbaric as separating children from their parents. One side wants walls and child separations to control the border, and the other side is disgusted by the inhumanity of those policies. Immigration Moneyball is a policy that could simultaneously humanize immigrants, control their admission, and create a merit‐​based immigration system that both sides of the political spectrum would support.

Justin Gest

Justin Gest is an associate professor of policy and government at George Mason University’s Schar School of Policy and Government. He is the author of four books and many academic journal articles. Previously, he was a Harvard College Fellow and lecturer on government and sociology at Harvard University, and he cofounded the Migration Studies Unit at the London School of Economics.

97 National Academies of Sciences, Engineering, and Medicine, The Economic and Fiscal Consequences of Immigration (Washington: The National Academies Press, 2017); and National Academies of Sciences, Engineering, and Medicine, The Integration of Immigrants into American Society (Washington: The National Academies Press, 2015).